#llmops
24 articles
Deploying LLM Workloads: GPUs, Kubernetes, and Serverless
Where LLM serving engines actually run: GPU cloud choices, Kubernetes patterns, serverless GPU tradeoffs, cold starts, and autoscaling strategies that hold up under real traffic.
From Prototype to Production: Scaling an AI Product
The concrete engineering decisions — model routing, caching strategy, async batching, context engineering, and cost governance — that turn a demo into a system handling real load.
Tracing and observability tooling: LangSmith, Langfuse, Braintrust, and Arize compared
A production-honest comparison of LangSmith, Langfuse, Braintrust, and Arize Phoenix for LLM tracing, evaluation, and quality monitoring in 2026.
Prompt Versioning, Evaluation, and LLMOps: Treating Prompts Like Code
How production teams version, test, and regression-test prompts the same way they ship software — CI/CD gates, eval datasets, and the tools that make it tractable.
The AI Engineer's Toolkit: Python, uv, and a Sane Dev Setup
Set up a Python AI engineering environment with uv, proper API key hygiene, and the minimal stack to start calling frontier models without the usual friction.
The data flywheel: closing the LLM feedback loop in production
How to run a data flywheel in production: capture implicit and explicit user signals, curate them into fine-tuning data, and compound model quality every cycle.
The LLM Gateway Pattern: Rate Limiting, Cost Attribution, and Observability
How a centralized AI gateway gives you virtual keys, dual TPM/RPM rate limiting, per-request cost tagging, and provider fallback — and when to build vs buy.
Prompt Anti-Patterns: The Failure Modes That Ship to Production Silently
Ten documented prompt defects — instruction-stacking, few-shot pollution, persona-stuffing — and why they survive code review and only surface under production load.
A/B Testing Prompts and Models in Production
How to run statistically valid experiments on LLM prompt variants and model swaps — shadow mode, canary rollout, LLM-as-judge scoring, and when to call the winner.
LLM Observability: Monitoring Quality, Cost, and Drift in Production
Traditional APM tells you the HTTP call succeeded. LLM observability tells you whether the answer was any good — covering traces, quality scores, cost anomalies, and drift.
Golden Datasets: Building the Ground Truth Your Evals Depend On
A concrete recipe for constructing and maintaining LLM evaluation datasets — sourcing, sizing, versioning, and wiring them into CI so regressions are caught before they ship.
Prompt Caching: Cutting Costs 50-90% Without Changing Model Behavior
How Anthropic and OpenAI prompt caching works, what breaks the cache, and how to structure prompts to hit 80%+ cache rates in production.
Prompt Versioning and CI/CD for LLM Changes
How to treat prompts as versioned, tested artifacts and build a deployment pipeline that gates every change on automated evaluation before it reaches users.
LLM-as-Judge: Calibrating Your Automated Evaluator
How to build an LLM judge that actually agrees with your domain experts — covering bias failure modes, rubric design, calibration targets, and production cost math.
The AI Gateway Pattern
How a thin proxy between your app and every LLM provider gives you routing, fallback, cost tracking, and semantic caching without touching application code.
Offline vs Online Evaluation: Building Your Two-Layer Safety Net
Pre-deployment regression suites catch what you break; production sampling catches what happens to you. Here is how to build both layers and wire them together.
SFT and Instruction Tuning: Data Is the Job
Supervised fine-tuning fundamentals with an honest look at why 1,000 curated examples reliably beat 100,000 scraped ones, and the training recipes that actually work.
System Prompts: Design Patterns That Actually Hold Up in Production
Four-section structure, cache-friendly ordering, modular composition, and how to write system prompts that survive model upgrades and adversarial users.
AI Engineer vs ML Engineer: what each role actually owns
AI Engineer is a distinct role from ML Engineer and AI Researcher. Learn the skill boundaries, daily work, and career tradeoffs between each in 2025-2026.
Reference architecture for a production LLM application
The seven-layer blueprint for production LLM apps: ingress, AI gateway, context assembly, model execution, output validation, observability, and feedback store.
From Prompt Engineering to Context Engineering: What Actually Changed
Why writing clever prompts is the wrong mental model for 2025 LLM systems, and how the context-window-as-RAM framing changes what you build.
Eval First: Building the Safety Net Before You Need It
Why LLM eval is hard, how 50 real examples beat 5,000 synthetic ones, and how to wire offline CI gates and production sampling into a two-layer safety net.
Cost Control and Reliability Engineering
Cut your LLM API bill ~4x and eliminate 429 cascades with prompt caching, model routing, semantic caching, and circuit breakers. Worked math from $60k to $14k/month.
LLMOps: Shipping and Operating AI Products
The operational layer that separates lasting AI products from perpetual pilots: gateways, eval pipelines, prompt CI/CD, observability, and the data flywheel.